{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分析Z500上各个模型的性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from src.utils.plot import subplot_daloop\n",
    "from src.utils.data_utils import NAME_TO_VAR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "FORECAST_DIR = \"../../results/afnonet\"\n",
    "ERA5_DIR = \"../../data/era5\"\n",
    "VARIABLE = \"geopotential\"\n",
    "LEVEL = 500\n",
    "RESOLUTION = 5.625"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gt = xr.open_mfdataset(f\"{ERA5_DIR}/{VARIABLE}_{LEVEL}_{RESOLUTION}deg/test/*.nc\", combine=\"by_coords\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制同化预报循环误差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "rmse_afnonet = xr.open_mfdataset(f\"{FORECAST_DIR}/rmse_afnonet.nc\", combine=\"by_coords\")[\"z\"].values\n",
    "\n",
    "acc_afnonet = xr.open_mfdataset(f\"{FORECAST_DIR}/acc_afnonet.nc\", combine=\"by_coords\")[\"z\"].values\n",
    "\n",
    "mae_afnonet = xr.open_mfdataset(f\"{FORECAST_DIR}/mae_afnonet.nc\", combine=\"by_coords\")[\"z\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x350 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(14, 3.5))\n",
    "axes[0].plot(np.arange(1, 29), rmse_afnonet[1:], label=f\"FourCastNet\")\n",
    "axes[1].plot(np.arange(1, 29), acc_afnonet[1:], label=f\"FourCastNet\")\n",
    "axes[2].plot(np.arange(1, 29), mae_afnonet[1:], label=f\"FourCastNet\")\n",
    "\n",
    "for j in range(3):\n",
    "    axes[j].set_xticks(np.arange(0, 32, 4))\n",
    "    axes[j].set_xticklabels([0, 1, 2, 3, 4, 5, 6, 7])\n",
    "\n",
    "axes[0].set_ylabel(f\"RMSE [m$^2$ s$^{-2}$]\")\n",
    "axes[1].set_ylabel(f\"ACC\")\n",
    "axes[2].set_ylabel(f\"MAE [m$^2$ s$^{-2}$]\")\n",
    "lines, labels = fig.axes[0].get_legend_handles_labels()\n",
    "# fig.legend(lines, labels, ncol=4, loc='lower center', bbox_to_anchor=(0.5, 0))\n",
    "fig.text(0.5, 0, f\"Lead Times [days]\", ha='center')\n",
    "plt.savefig(f\"fourcastnet.pdf\",dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of 4DVar Obs20:  909.18896\n",
      "RMSE of 4DVarNet Obs20:  894.613\n",
      "RMSE of ViT Obs20:  847.91077\n",
      "RMSE of 4DVarGAN Obs20:  819.5166\n",
      "RMSE of 4DVar Obs15:  919.23883\n",
      "RMSE of 4DVarNet Obs15:  888.1757\n",
      "RMSE of ViT Obs15:  848.1637\n",
      "RMSE of 4DVarGAN Obs15:  813.0814\n",
      "RMSE of 4DVar Obs10:  930.6613\n",
      "RMSE of 4DVarNet Obs10:  892.796\n",
      "RMSE of ViT Obs10:  868.1948\n",
      "RMSE of 4DVarGAN Obs10:  819.7487\n",
      "RMSE of 4DVar Obs5:  934.9359\n",
      "RMSE of 4DVarNet Obs5:  890.20874\n",
      "RMSE of ViT Obs5:  911.8728\n",
      "RMSE of 4DVarGAN Obs5:  832.5917\n"
     ]
    }
   ],
   "source": [
    "for k in rmse.keys():\n",
    "    print(f\"RMSE of {k}: \", rmse[k][12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE of 4DVar Obs20:  949.4355\n",
      "RMSE of 4DVarNet Obs20:  980.1853\n",
      "RMSE of ViT Obs20:  920.8076\n",
      "RMSE of 4DVarGAN Obs20:  887.0472\n",
      "RMSE of 4DVar Obs15:  959.31616\n",
      "RMSE of 4DVarNet Obs15:  981.20074\n",
      "RMSE of ViT Obs15:  915.03644\n",
      "RMSE of 4DVarGAN Obs15:  889.9466\n",
      "RMSE of 4DVar Obs10:  954.17584\n",
      "RMSE of 4DVarNet Obs10:  993.7281\n",
      "RMSE of ViT Obs10:  932.4083\n",
      "RMSE of 4DVarGAN Obs10:  888.6272\n",
      "RMSE of 4DVar Obs5:  965.2456\n",
      "RMSE of 4DVarNet Obs5:  987.3037\n",
      "RMSE of ViT Obs5:  961.55176\n",
      "RMSE of 4DVarGAN Obs5:  895.40375\n"
     ]
    }
   ],
   "source": [
    "for k in rmse.keys():\n",
    "    print(f\"RMSE of {k}: \", rmse[k][20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACC of 4DVar Obs20:  0.5475849\n",
      "ACC of 4DVarNet Obs20:  0.5836131\n",
      "ACC of ViT Obs20:  0.5780347\n",
      "ACC of 4DVarGAN Obs20:  0.6111761\n",
      "ACC of 4DVar Obs15:  0.5393518\n",
      "ACC of 4DVarNet Obs15:  0.58860123\n",
      "ACC of ViT Obs15:  0.5753562\n",
      "ACC of 4DVarGAN Obs15:  0.61513484\n",
      "ACC of 4DVar Obs10:  0.5263347\n",
      "ACC of 4DVarNet Obs10:  0.58249927\n",
      "ACC of ViT Obs10:  0.5605047\n",
      "ACC of 4DVarGAN Obs10:  0.6041089\n",
      "ACC of 4DVar Obs5:  0.51477045\n",
      "ACC of 4DVarNet Obs5:  0.586687\n",
      "ACC of ViT Obs5:  0.5311745\n",
      "ACC of 4DVarGAN Obs5:  0.58368033\n"
     ]
    }
   ],
   "source": [
    "for k in acc.keys():\n",
    "    print(f\"ACC of {k}: \", acc[k][12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACC of 4DVar Obs20:  0.4918363\n",
      "ACC of 4DVarNet Obs20:  0.49595845\n",
      "ACC of ViT Obs20:  0.5014532\n",
      "ACC of 4DVarGAN Obs20:  0.52976924\n",
      "ACC of 4DVar Obs15:  0.48526853\n",
      "ACC of 4DVarNet Obs15:  0.4943774\n",
      "ACC of ViT Obs15:  0.5078161\n",
      "ACC of 4DVarGAN Obs15:  0.5224134\n",
      "ACC of 4DVar Obs10:  0.48801836\n",
      "ACC of 4DVarNet Obs10:  0.48268947\n",
      "ACC of ViT Obs10:  0.49860084\n",
      "ACC of 4DVarGAN Obs10:  0.5243077\n",
      "ACC of 4DVar Obs5:  0.46968853\n",
      "ACC of 4DVarNet Obs5:  0.48801714\n",
      "ACC of ViT Obs5:  0.4895269\n",
      "ACC of 4DVarGAN Obs5:  0.5059836\n"
     ]
    }
   ],
   "source": [
    "for k in acc.keys():\n",
    "    print(f\"ACC of {k}: \", acc[k][20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE of 4DVar Obs20:  570.33386\n",
      "MAE of 4DVarNet Obs20:  584.9966\n",
      "MAE of ViT Obs20:  548.9095\n",
      "MAE of 4DVarGAN Obs20:  506.84485\n",
      "MAE of 4DVar Obs15:  575.3709\n",
      "MAE of 4DVarNet Obs15:  582.3152\n",
      "MAE of ViT Obs15:  551.8627\n",
      "MAE of 4DVarGAN Obs15:  504.55573\n",
      "MAE of 4DVar Obs10:  583.6844\n",
      "MAE of 4DVarNet Obs10:  588.7227\n",
      "MAE of ViT Obs10:  564.9433\n",
      "MAE of 4DVarGAN Obs10:  508.05197\n",
      "MAE of 4DVar Obs5:  586.21387\n",
      "MAE of 4DVarNet Obs5:  588.667\n",
      "MAE of ViT Obs5:  594.6886\n",
      "MAE of 4DVarGAN Obs5:  517.5474\n"
     ]
    }
   ],
   "source": [
    "for k in mae.keys():\n",
    "    print(f\"MAE of {k}: \", mae[k][12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE of 4DVar Obs20:  595.7355\n",
      "MAE of 4DVarNet Obs20:  637.11346\n",
      "MAE of ViT Obs20:  593.29535\n",
      "MAE of 4DVarGAN Obs20:  552.11194\n",
      "MAE of 4DVar Obs15:  602.5839\n",
      "MAE of 4DVarNet Obs15:  636.43427\n",
      "MAE of ViT Obs15:  591.52185\n",
      "MAE of 4DVarGAN Obs15:  553.98975\n",
      "MAE of 4DVar Obs10:  601.3027\n",
      "MAE of 4DVarNet Obs10:  648.5493\n",
      "MAE of ViT Obs10:  605.4609\n",
      "MAE of 4DVarGAN Obs10:  552.9168\n",
      "MAE of 4DVar Obs5:  606.95575\n",
      "MAE of 4DVarNet Obs5:  645.7746\n",
      "MAE of ViT Obs5:  627.07776\n",
      "MAE of 4DVarGAN Obs5:  559.1914\n"
     ]
    }
   ],
   "source": [
    "for k in mae.keys():\n",
    "    print(f\"MAE of {k}: \", mae[k][20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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